{"title":"映射。生物:为生物多样性数字双胞胎试点FAIR语义映射","authors":"Alexander Wolodkin, Claus Weiland, Jonas Grieb","doi":"10.3897/biss.7.111979","DOIUrl":null,"url":null,"abstract":"Biodiversity research has a strong focus on the links between environment and functional traits, e.g., to assess how anthropogenic drivers of change impact ecological systems (Díaz et al. 2013). Interoperable exchange and integration of such data is enabled through the use of ontologies that provide ”meaning” to data and enable downstream processing involving learning and inference over graph-structured models of these data (Kulmanov et al. 2020). However, the development of thematically similar semantic artifacts, e.g., the Environmental Ontology (ENVO, Buttigieg et al. 2016) and the Semantic Web for Earth and Environment Technology Ontology (SWEET, DiGiuseppe et al. 2014), in biodiversity-related disciplines (e.g., environmental genomics and earth observation) can introduce substantial conceptual overlaps, and highlights the need for bridging technologies to facilitate reuse of biodiversity data across those knowledge fields (Karam et al. 2020). \n A recent design study, funded by the European Open Science Cloud (EOSC), proposes a framework to create, document and publish mappings and crosswalks linking different semantic artifacts within a particular scientific community and across scientific domains under the label of \"Flexible Semantic Mapping Framework\" (SEMAF, Broeder et al. 2021). SEMAF puts a strong emphasis on so-called pragmatic mappings, i.e., mappings that are driven by specific interoperability goals such as translations between specific observation measurements (e.g., sensor configurations) and metadata descriptions. Within the Horizon Europe Project “Biodiversity Digital Twin for Advanced Modelling, Simulation and Prediction Capabilities\" (BioDT), a mapping tool leveraging SEMAF is currently under development: Mapping.bio provides a lightweight web service to read semantic artifacts, visualize them, add mappings as graphical connections and store the mappings as FAIR (Findable, Accessible, Interoperable Reusable) Digital Objects (FDOs, De Smedt et al. 2020) in a repository. To foster reusability, sustainably and long-term availability of digital objects, mapping.bio features mappings compliant with the Simple Standard for Sharing Ontological Mappings (SSSOM, Matentzoglu et al. 2022), a machine-interpretable and extensible vocabulary enabling the self-contained exploration and processing of annotated mappings by machines (machine actionability, Jacobsen et al. 2020).","PeriodicalId":9011,"journal":{"name":"Biodiversity Information Science and Standards","volume":"24 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Mapping.bio: Piloting FAIR semantic mappings for biodiversity digital twins\",\"authors\":\"Alexander Wolodkin, Claus Weiland, Jonas Grieb\",\"doi\":\"10.3897/biss.7.111979\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Biodiversity research has a strong focus on the links between environment and functional traits, e.g., to assess how anthropogenic drivers of change impact ecological systems (Díaz et al. 2013). Interoperable exchange and integration of such data is enabled through the use of ontologies that provide ”meaning” to data and enable downstream processing involving learning and inference over graph-structured models of these data (Kulmanov et al. 2020). However, the development of thematically similar semantic artifacts, e.g., the Environmental Ontology (ENVO, Buttigieg et al. 2016) and the Semantic Web for Earth and Environment Technology Ontology (SWEET, DiGiuseppe et al. 2014), in biodiversity-related disciplines (e.g., environmental genomics and earth observation) can introduce substantial conceptual overlaps, and highlights the need for bridging technologies to facilitate reuse of biodiversity data across those knowledge fields (Karam et al. 2020). \\n A recent design study, funded by the European Open Science Cloud (EOSC), proposes a framework to create, document and publish mappings and crosswalks linking different semantic artifacts within a particular scientific community and across scientific domains under the label of \\\"Flexible Semantic Mapping Framework\\\" (SEMAF, Broeder et al. 2021). SEMAF puts a strong emphasis on so-called pragmatic mappings, i.e., mappings that are driven by specific interoperability goals such as translations between specific observation measurements (e.g., sensor configurations) and metadata descriptions. Within the Horizon Europe Project “Biodiversity Digital Twin for Advanced Modelling, Simulation and Prediction Capabilities\\\" (BioDT), a mapping tool leveraging SEMAF is currently under development: Mapping.bio provides a lightweight web service to read semantic artifacts, visualize them, add mappings as graphical connections and store the mappings as FAIR (Findable, Accessible, Interoperable Reusable) Digital Objects (FDOs, De Smedt et al. 2020) in a repository. 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引用次数: 0
摘要
生物多样性研究非常关注环境与功能特征之间的联系,例如,评估变化的人为驱动因素如何影响生态系统(Díaz et al. 2013)。这些数据的可互操作交换和集成是通过使用本体来实现的,本体为数据提供“意义”,并支持下游处理,包括对这些数据的图结构模型的学习和推理(Kulmanov et al. 2020)。然而,在生物多样性相关学科(如环境基因组学和地球观测)中开发主题相似的语义工件,例如环境本体(ENVO, Buttigieg等人,2016)和地球与环境技术本体语义网(SWEET, DiGiuseppe等人,2014),可以引入大量的概念重叠。并强调需要桥接技术来促进跨这些知识领域的生物多样性数据重用(Karam et al. 2020)。最近由欧洲开放科学云(EOSC)资助的一项设计研究提出了一个框架,用于在“灵活语义映射框架”(SEMAF, Broeder et al. 2021)的标签下创建、记录和发布连接特定科学社区和跨科学领域内不同语义工件的映射和人行横道。SEMAF非常强调所谓的实用映射,即由特定互操作性目标驱动的映射,例如特定观测测量(例如,传感器配置)和元数据描述之间的转换。在地平线欧洲项目“生物多样性数字孪生用于高级建模、模拟和预测能力”(BioDT)中,目前正在开发一种利用SEMAF的绘图工具:mapping。bio提供了一个轻量级的web服务来读取语义工件,将它们可视化,将映射添加为图形连接,并将映射存储为存储库中的FAIR(可查找、可访问、可互操作、可重用)数字对象(fdo, De Smedt等人,2020)。促进数字对象的可重用性、可持续性和长期可用性。生物特征映射符合共享本体论映射的简单标准(SSSOM, Matentzoglu et al. 2022),这是一种机器可解释和可扩展的词汇表,允许机器对注释映射进行独立的探索和处理(机器可操作性,Jacobsen et al. 2020)。
Mapping.bio: Piloting FAIR semantic mappings for biodiversity digital twins
Biodiversity research has a strong focus on the links between environment and functional traits, e.g., to assess how anthropogenic drivers of change impact ecological systems (Díaz et al. 2013). Interoperable exchange and integration of such data is enabled through the use of ontologies that provide ”meaning” to data and enable downstream processing involving learning and inference over graph-structured models of these data (Kulmanov et al. 2020). However, the development of thematically similar semantic artifacts, e.g., the Environmental Ontology (ENVO, Buttigieg et al. 2016) and the Semantic Web for Earth and Environment Technology Ontology (SWEET, DiGiuseppe et al. 2014), in biodiversity-related disciplines (e.g., environmental genomics and earth observation) can introduce substantial conceptual overlaps, and highlights the need for bridging technologies to facilitate reuse of biodiversity data across those knowledge fields (Karam et al. 2020).
A recent design study, funded by the European Open Science Cloud (EOSC), proposes a framework to create, document and publish mappings and crosswalks linking different semantic artifacts within a particular scientific community and across scientific domains under the label of "Flexible Semantic Mapping Framework" (SEMAF, Broeder et al. 2021). SEMAF puts a strong emphasis on so-called pragmatic mappings, i.e., mappings that are driven by specific interoperability goals such as translations between specific observation measurements (e.g., sensor configurations) and metadata descriptions. Within the Horizon Europe Project “Biodiversity Digital Twin for Advanced Modelling, Simulation and Prediction Capabilities" (BioDT), a mapping tool leveraging SEMAF is currently under development: Mapping.bio provides a lightweight web service to read semantic artifacts, visualize them, add mappings as graphical connections and store the mappings as FAIR (Findable, Accessible, Interoperable Reusable) Digital Objects (FDOs, De Smedt et al. 2020) in a repository. To foster reusability, sustainably and long-term availability of digital objects, mapping.bio features mappings compliant with the Simple Standard for Sharing Ontological Mappings (SSSOM, Matentzoglu et al. 2022), a machine-interpretable and extensible vocabulary enabling the self-contained exploration and processing of annotated mappings by machines (machine actionability, Jacobsen et al. 2020).